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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        About MultiQC

        This report was generated using MultiQC, version 1.30

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the bigbio/quantms analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2025-07-16, 08:31 UTC based on data in: /home/runner/work/pmultiqc/pmultiqc/data

        pmultiqc

        pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant, and DIA-NN.URL: https://github.com/bigbio/pmultiqc


        Experiment Setup

        Experimental Design

        This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.
        You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html
        Showing 11/11 rows and 8/8 columns.
        Sample NameMSstats Condition: SPMSstats Condition: CTMSstats Condition: QYMSstats Condition: CVMSstats BioReplicateFraction GroupFractionLabel
         
        1
        Saccharomyces cerevisiaeMixture10%in-house1
         
         ↳ a05058
        111
         
         ↳ a05059
        121
         
         ↳ a05060
        131
         
         ↳ a05061
        141
         
         ↳ a05062
        151
         
         ↳ a05063
        161
         
         ↳ a05064
        171
         
         ↳ a05065
        181
         
         ↳ a05066
        191
         
         ↳ a05067
        1101
         
         ↳ a05068
        1111
         
        2
        Saccharomyces cerevisiaeMixture10%in-house2
         
         ↳ a05058
        112
         
         ↳ a05059
        122
         
         ↳ a05060
        132
         
         ↳ a05061
        142
         
         ↳ a05062
        152
         
         ↳ a05063
        162
         
         ↳ a05064
        172
         
         ↳ a05065
        182
         
         ↳ a05066
        192
         
         ↳ a05067
        1102
         
         ↳ a05068
        1112
         
        3
        Saccharomyces cerevisiaeMixture10%in-house3
         
         ↳ a05058
        113
         
         ↳ a05059
        123
         
         ↳ a05060
        133
         
         ↳ a05061
        143
         
         ↳ a05062
        153
         
         ↳ a05063
        163
         
         ↳ a05064
        173
         
         ↳ a05065
        183
         
         ↳ a05066
        193
         
         ↳ a05067
        1103
         
         ↳ a05068
        1113
         
        4
        Saccharomyces cerevisiaeMixture5%in-house4
         
         ↳ a05058
        114
         
         ↳ a05059
        124
         
         ↳ a05060
        134
         
         ↳ a05061
        144
         
         ↳ a05062
        154
         
         ↳ a05063
        164
         
         ↳ a05064
        174
         
         ↳ a05065
        184
         
         ↳ a05066
        194
         
         ↳ a05067
        1104
         
         ↳ a05068
        1114
         
        5
        Saccharomyces cerevisiaeMixture5%in-house5
         
         ↳ a05058
        115
         
         ↳ a05059
        125
         
         ↳ a05060
        135
         
         ↳ a05061
        145
         
         ↳ a05062
        155
         
         ↳ a05063
        165
         
         ↳ a05064
        175
         
         ↳ a05065
        185
         
         ↳ a05066
        195
         
         ↳ a05067
        1105
         
         ↳ a05068
        1115
         
        6
        Saccharomyces cerevisiaeMixture5%in-house6
         
         ↳ a05058
        116
         
         ↳ a05059
        126
         
         ↳ a05060
        136
         
         ↳ a05061
        146
         
         ↳ a05062
        156
         
         ↳ a05063
        166
         
         ↳ a05064
        176
         
         ↳ a05065
        186
         
         ↳ a05066
        196
         
         ↳ a05067
        1106
         
         ↳ a05068
        1116
         
        7
        Saccharomyces cerevisiaeMixture5%in-house7
         
         ↳ a05058
        117
         
         ↳ a05059
        127
         
         ↳ a05060
        137
         
         ↳ a05061
        147
         
         ↳ a05062
        157
         
         ↳ a05063
        167
         
         ↳ a05064
        177
         
         ↳ a05065
        187
         
         ↳ a05066
        197
         
         ↳ a05067
        1107
         
         ↳ a05068
        1117
         
        8
        Saccharomyces cerevisiaeMixture3.3%in-house8
         
         ↳ a05058
        118
         
         ↳ a05059
        128
         
         ↳ a05060
        138
         
         ↳ a05061
        148
         
         ↳ a05062
        158
         
         ↳ a05063
        168
         
         ↳ a05064
        178
         
         ↳ a05065
        188
         
         ↳ a05066
        198
         
         ↳ a05067
        1108
         
         ↳ a05068
        1118
         
        9
        Saccharomyces cerevisiaeMixture3.3%in-house9
         
         ↳ a05058
        119
         
         ↳ a05059
        129
         
         ↳ a05060
        139
         
         ↳ a05061
        149
         
         ↳ a05062
        159
         
         ↳ a05063
        169
         
         ↳ a05064
        179
         
         ↳ a05065
        189
         
         ↳ a05066
        199
         
         ↳ a05067
        1109
         
         ↳ a05068
        1119
         
        10
        Saccharomyces cerevisiaeMixture3.3%in-house10
         
         ↳ a05058
        1110
         
         ↳ a05059
        1210
         
         ↳ a05060
        1310
         
         ↳ a05061
        1410
         
         ↳ a05062
        1510
         
         ↳ a05063
        1610
         
         ↳ a05064
        1710
         
         ↳ a05065
        1810
         
         ↳ a05066
        1910
         
         ↳ a05067
        11010
         
         ↳ a05068
        11110
         
        11
        Saccharomyces cerevisiaeMixture3.3%in-house11
         
         ↳ a05058
        1111
         
         ↳ a05059
        1211
         
         ↳ a05060
        1311
         
         ↳ a05061
        1411
         
         ↳ a05062
        1511
         
         ↳ a05063
        1611
         
         ↳ a05064
        1711
         
         ↳ a05065
        1811
         
         ↳ a05066
        1911
         
         ↳ a05067
        11011
         
         ↳ a05068
        11111

        Summary and HeatMap

        Summary Table

        This table shows the quantms pipeline summary statistics.
        This table shows the quantms pipeline summary statistics.
        Showing 1/1 rows and 5/5 columns.
        #MS2 Spectra#Identified MS2 Spectra%Identified MS2 Spectra#Peptides Identified#Proteins Identified#Proteins Quantified
        433836
        154772
        35.68%
        90555
        9655
        9655

        HeatMap

        This heatmap provides an overview of the performance of the quantms.
        This plot shows the pipeline performance overview. Some metrics are calculated. * Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants * Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25. * Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544). * Heatmap score [Missed Cleavages]: the fraction (0% - 100%) of fully cleaved peptides per Raw file * Heatmap score [Missed Cleavages Var]: each Raw file is scored for its deviation from the ‘average’ digestion state of the current study. * Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad. * Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. * Heatmap score [Pep Missing Values]: Linear scale of the fraction of missing peptides.
        Created with MultiQC

        Pipeline Result Statistics

        This plot shows the quantms pipeline final result.
        Including Sample Name, Possible Study Variables, identified the number of peptide in the pipeline, and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the `remove_decoy` parameter.
        Showing 11/11 rows and 9/9 columns.
        Sample NameMSstats Condition: SPMSstats Condition: CTMSstats Condition: QYMSstats Condition: CVFraction#Peptide IDs#Unambiguous Peptide IDs#Modified Peptide IDs#Protein (group) IDs
         
        1
        Saccharomyces cerevisiaeMixture10%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        2
        Saccharomyces cerevisiaeMixture10%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        3
        Saccharomyces cerevisiaeMixture10%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        4
        Saccharomyces cerevisiaeMixture5%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        5
        Saccharomyces cerevisiaeMixture5%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        6
        Saccharomyces cerevisiaeMixture5%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        7
        Saccharomyces cerevisiaeMixture5%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        8
        Saccharomyces cerevisiaeMixture3.3%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        9
        Saccharomyces cerevisiaeMixture3.3%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        10
        Saccharomyces cerevisiaeMixture3.3%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10
         
        11
        Saccharomyces cerevisiaeMixture3.3%in-house
         
         ↳ a05058
        1
        12495
        11321
        12495
        5631
         
         ↳ a05059
        2
        13568
        12325
        13568
        6045
         
         ↳ a05060
        3
        13003
        11783
        13003
        5875
         
         ↳ a05061
        4
        13433
        12260
        13433
        5902
         
         ↳ a05062
        5
        14216
        12956
        14216
        6159
         
         ↳ a05063
        6
        18009
        16369
        18009
        6420
         
         ↳ a05064
        7
        16285
        14718
        16285
        6068
         
         ↳ a05065
        8
        14215
        13004
        14215
        6077
         
         ↳ a05066
        9
        14554
        13271
        14554
        6093
         
         ↳ a05067
        10
        13765
        12537
        13765
        6024
         
         ↳ a05068
        11
        12
        12
        12
        10

        Identification Summary

        Number of Peptides identified Per Protein

        This plot shows the number of peptides per protein in quantms pipeline final result
        This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.
        Created with MultiQC

        ProteinGroups Count

        Number of protein groups per raw file.
        Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
        Created with MultiQC

        Peptide ID Count

        Number of unique (i.e. not counted twice) peptide sequences including modifications per Raw file.
        Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
        Created with MultiQC

        Missed Cleavages Per Raw File

        Missed Cleavages per raw file.
        Under optimal digestion conditions (high enzyme grade etc.), only few missed cleavages (MC) are expected. In general, increased MC counts also increase the number of peptide signals, thus cluttering the available space and potentially provoking overlapping peptide signals, biasing peptide quantification. Thus, low MC counts should be favored. Interestingly, it has been shown recently that incorporation of peptides with missed cleavages does not negatively influence protein quantification (see [Chiva, C., Ortega, M., and Sabido, E. Influence of the Digestion Technique, Protease, and Missed Cleavage Peptides in Protein Quantitation. J. Proteome Res. 2014, 13, 3979-86](https://doi.org/10.1021/pr500294d) ). However this is true only if all samples show the same degree of digestion. High missed cleavage values can indicate for example, either a) failed digestion, b) a high (post-digestion) protein contamination, or c) a sample with high amounts of unspecifically degraded peptides which are not digested by trypsin. If MC>=1 is high (>20%) you should increase the missed cleavages settings in MaxQuant and compare the number of peptides. Usually high MC correlates with bad identification rates, since many spectra cannot be matched to the forward database. In the rare case that 'no enzyme' was specified in MaxQuant, neither scores nor plots are shown.
        Created with MultiQC

        Modifications Per Raw File

        Compute an occurence table of modifications (e.g. Oxidation (M)) for all peptides, including the unmodified.
        Post-translational modifications contained within the identified peptide sequence.
        Created with MultiQC

        MS/MS Identified Per Raw File

        MS/MS identification rate per Raw file.
        MS/MS identification rate per raw file (quantms data from mzTab and mzML files; MaxQuant data from summary.txt)
        Created with MultiQC

        Search Engine Scores

        Summary of Spectral E-values

        This statistic is extracted from idXML files. SpecEvalue: Spectral E-values, the search score of MSGF. The value used for plotting is -lg(SpecEvalue).
        Created with MultiQC

        Summary of cross-correlation scores

        This statistic is extracted from idXML files. xcorr: cross-correlation scores, the search score of Comet. The value used for plotting is xcorr.
        Created with MultiQC

        Summary of Search Engine PEP

        This statistic is extracted from idXML files.
        Created with MultiQC

        Consensus Across Search Engines

        Consensus support is a measure of agreement between search engines. Every peptide sequence in the analysis has been identified by at least one search run. The consensus support defines which fraction (between 0 and 1) of the remaining search runs "supported" a peptide identification that was kept. The meaning of "support" differs slightly between algorithms: For best, worst, average and rank, each search run supports peptides that it has also identified among its top considered_hits candidates. So the "consensus support" simply gives the fraction of additional search engines that have identified a peptide. (For example, if there are three search runs, peptides identified by two of them will have a "support" of 0.5.) For the similarity-based algorithms PEPMatrix and PEPIons, the "support" for a peptide is the average similarity of the most-similar peptide from each (other) search run.
        Created with MultiQC

        Quantification Analysis

        Peptides Quantification Table

        This plot shows the quantification information of peptides in the final result (mainly the mzTab file).
        The quantification information of peptides is obtained from the MSstats input file. The table shows the quantitative level and distribution of peptides in different study variables, run and peptiforms. The distribution show all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptiforms. * BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to 1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab peptide table for DDA datasets. * Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored. * Peptide intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of fractions, and then mean intensity in technical replicates/biological replicates separately.
        Showing 50/50 rows and 7/7 columns.
        PeptideIDProtein NamePeptide SequenceBest Search ScoreAverage IntensitySP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-houseSP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-houseSP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house
        1
        sp|P55011|S12A2_HUMAN
        AAAAAAAAAAAAAAAGAGAGAK
        0.9999
        5.1101
        5.0860
        5.1404
        5.0960
        2
        sp|Q99453|PHX2B_HUMAN
        AAAAAAAAAK
        1.0000
        5.9295
        5.9276
        5.9481
        5.9117
        3
        sp|Q96JP5|ZFP91_HUMAN
        AAAAAAAAAVSR
        1.0000
        4.2500
        4.2237
        4.2531
        4.2656
        4
        sp|Q5TF21|SOGA3_HUMAN
        AAAAAAAAQMHAK
        1.0000
        4.9208
        4.8813
        4.9496
        4.9193
        5
        sp|Q9Y4H2|IRS2_HUMAN
        AAAAAAAAVPSAGPAGPAPTSAAGR
        1.0000
        4.6795
        4.6525
        4.7113
        4.6659
        6
        sp|P36578|RL4_HUMAN
        AAAAAAALQAK
        1.0000
        5.6602
        5.6780
        5.6812
        5.6235
        7
        sp|O00178|GTPB1_HUMAN
        AAAAAAR
        0.9997
        4.1917
        4.0798
        4.2682
        4.1818
        8
        sp|Q6SPF0|SAMD1_HUMAN
        AAAAAATAPPSPGPAQPGPR
        0.9970
        4.5783
        4.5565
        4.5829
        4.5895
        9
        sp|O76031|CLPX_HUMAN
        AAAAADLANR
        1.0000
        4.9044
        4.8651
        4.9259
        4.9106
        10
        sp|Q8WUQ7|CATIN_HUMAN
        AAAAALSQQQSLQER
        1.0000
        3.5486
        3.4986
        3.5609
        3.5710
        11
        sp|A6NIH7|U119B_HUMAN
        AAAAASAAGPGGLVAGK
        1.0000
        4.5009
        4.4867
        4.5216
        4.4900
        12
        sp|Q9BTD8|RBM42_HUMAN
        AAAAATVVPPMVGGPPFVGPVGFGPGDR
        0.9997
        4.1653
        4.1245
        4.1983
        4.1603
        13
        sp|Q9P258|RCC2_HUMAN
        AAAAAWEEPSSGNGTAR
        1.0000
        4.8387
        4.8197
        4.8616
        4.8291
        14
        sp|Q99615|DNJC7_HUMAN
        AAAAECDVVMAATEPELLDDQEAK
        0.9989
        4.3740
        4.3483
        4.3851
        4.3813
        15
        sp|Q9Y2U8|MAN1_HUMAN
        AAAAGSLDR
        0.9999
        4.9444
        4.9107
        4.9686
        4.9437
        16
        sp|P0C0T4|RS25B_YEAST;sp|Q3E792|RS25A_YEAST
        AAAALAGGK
        0.9999
        5.8076
        6.0928
        5.7184
        5.4974
        17
        sp|Q15596|NCOA2_HUMAN
        AAAANIDEVQK
        1.0000
        4.1618
        4.1756
        4.1710
        4.1415
        18
        sp|Q9UPT8|ZC3H4_HUMAN
        AAAAPAATTATPPPEGAPPQPGVHNLPVPTLFGTVK
        1.0000
        4.4684
        4.4335
        4.5097
        4.4498
        19
        sp|Q96L91|EP400_HUMAN
        AAAAPFQTSQASASAPR
        0.9997
        4.2268
        4.1954
        4.2729
        4.2001
        20
        sp|P52701|MSH6_HUMAN
        AAAAPGASPSPGGDAAWSEAGPGPR
        1.0000
        5.3677
        5.4158
        5.3708
        5.3244
        21
        sp|P52701|MSH6_HUMAN
        AAAAPGASPSPGGDAAWSEAGPGPRPLAR
        1.0000
        3.5832
        3.5465
        3.6371
        3.5511
        22
        sp|P55036|PSMD4_HUMAN
        AAAASAAEAGIATTGTEDSDDALLK
        1.0000
        4.9622
        4.9446
        4.9947
        4.9408
        23
        sp|P09938|RIR2_YEAST
        AAADALSDLEIK
        0.9997
        4.8986
        5.1140
        4.8541
        4.6881
        24
        sp|O95159|ZFPL1_HUMAN
        AAADSDPNLDPLMNPHIR
        1.0000
        5.1045
        5.0664
        5.1372
        5.0980
        25
        sp|Q6P2E9|EDC4_HUMAN
        AAADTLQGPMQAAYR
        1.0000
        3.6096
        3.5992
        3.6228
        3.6039
        26
        sp|Q6P2E9|EDC4_HUMAN
        AAADTLQGPMQAAYR
        1.0000
        3.3063
        3.2023
        3.3229
        3.3557
        27
        sp|Q9NQP4|PFD4_HUMAN
        AAAEDVNVTFEDQQK
        1.0000
        4.0292
        3.9906
        4.0576
        4.0276
        28
        sp|O95551|TYDP2_HUMAN
        AAAEEGHIIPR
        1.0000
        5.4478
        5.3897
        5.4833
        5.4515
        29
        sp|O15357|SHIP2_HUMAN
        AAAEELLAR
        1.0000
        5.0724
        5.0563
        5.0820
        5.0745
        30
        sp|Q9H9Y2|RPF1_HUMAN
        AAAEELQEAAGAGDGATENGVQPPK
        1.0000
        4.5297
        4.4950
        4.5624
        4.5206
        31
        sp|Q96P70|IPO9_HUMAN
        AAAEEQIK
        0.9999
        5.2276
        5.1954
        5.2506
        5.2273
        32
        sp|P30260|CDC27_HUMAN
        AAAEGLMSLLR
        1.0000
        4.3024
        4.2771
        4.3361
        4.2854
        33
        sp|P00360|G3P1_YEAST
        AAAEGPMK
        0.9988
        3.9138
        4.1444
        3.8283
        3.7290
        34
        sp|P00360|G3P1_YEAST
        AAAEGPMK
        1.0000
        4.7633
        5.0495
        4.6743
        4.4495
        35
        sp|P15180|SYKC_YEAST
        AAAEGVANLHLDEATGEMVSK
        1.0000
        4.2037
        4.4023
        4.1753
        4.0020
        36
        sp|P15180|SYKC_YEAST
        AAAEGVANLHLDEATGEMVSK
        1.0000
        5.0252
        5.2850
        4.9685
        4.7316
        37
        sp|Q9NP50|SHCAF_HUMAN
        AAAEKPEEQGPEPLPISTQEW
        1.0000
        4.6442
        4.6190
        4.6637
        4.6425
        38
        sp|Q01780|EXOSX_HUMAN
        AAAEQAISVR
        1.0000
        4.9211
        4.9080
        4.9425
        4.9085
        39
        sp|O94766|B3GA3_HUMAN
        AAAEQLR
        0.9998
        4.8666
        4.8356
        4.8987
        4.8555
        40
        sp|Q02880|TOP2B_HUMAN
        AAAERPK
        0.9991
        5.2989
        5.2501
        5.3288
        5.3027
        41
        sp|P02786|TFR1_HUMAN
        AAAEVAGQFVIK
        0.9999
        5.7815
        5.7528
        5.8144
        5.7681
        42
        sp|P07954|FUMH_HUMAN
        AAAEVNQDYGLDPK
        1.0000
        5.1767
        5.1675
        5.1999
        5.1594
        43
        sp|P15705|STI1_YEAST
        AAAEYEK
        1.0000
        4.8083
        5.0531
        4.7440
        4.5642
        44
        sp|Q9Y490|TLN1_HUMAN
        AAAFEEQENETVVVK
        1.0000
        5.1944
        5.1751
        5.2089
        5.1939
        45
        sp|Q9P2E9|RRBP1_HUMAN
        AAAFEK
        0.9998
        5.5448
        5.5160
        5.5730
        5.5365
        46
        sp|O94826|TOM70_HUMAN
        AAAFEQLQK
        1.0000
        5.0643
        5.0542
        5.0947
        5.0397
        47
        sp|Q9H9Y2|RPF1_HUMAN
        AAAFPPGFSISEIK
        1.0000
        4.4674
        4.4606
        4.4868
        4.4522
        48
        sp|Q9Y6X3|SCC4_HUMAN
        AAAFYVR
        0.9997
        4.4727
        4.4467
        4.4918
        4.4721
        49
        sp|Q9P2E9|RRBP1_HUMAN
        AAAGEAK
        1.0000
        4.3334
        4.2981
        4.3611
        4.3302
        50
        sp|P23381|SYWC_HUMAN
        AAAGEDYK
        1.0000
        5.1423
        5.1080
        5.1395
        5.1689

        Protein Quantification Table

        This plot shows the quantification information of proteins in the final result (mainly the mzTab file).
        The quantification information of proteins is obtained from the msstats input file. The table shows the quantitative level and distribution of proteins in different study variables and run. * Peptides_Number: The number of peptides for each protein. * Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored. * Protein intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of peptides.
        Showing 50/50 rows and 6/6 columns.
        ProteinIDProtein NameNumber of PeptidesAverage IntensitySP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-houseSP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-houseSP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house
        1
        sp|A0A096LP55|QCR6L_HUMAN;sp|P07919|QCR6_HUMAN
        1
        3.7629
        3.7228
        3.7953
        3.7581
        2
        sp|A0A0B4J2D5|GAL3B_HUMAN;sp|P0DPI2|GAL3A_HUMAN
        9
        6.5573
        6.5406
        6.5786
        6.5475
        3
        sp|A0A0B4J2F0|PIOS1_HUMAN
        1
        4.2780
        4.2463
        4.3042
        4.2739
        4
        sp|A0AVF1|IFT56_HUMAN
        3
        4.4387
        4.4337
        4.4436
        4.4376
        5
        sp|A0AVT1|UBA6_HUMAN
        31
        6.7475
        6.7247
        6.7693
        6.7418
        6
        sp|A0FGR8|ESYT2_HUMAN
        15
        6.2588
        6.2497
        6.2776
        6.2462
        7
        sp|A0JNW5|UH1BL_HUMAN
        5
        5.2052
        5.1740
        5.2309
        5.2013
        8
        sp|A0MZ66|SHOT1_HUMAN
        27
        6.3095
        6.2787
        6.3350
        6.3057
        9
        sp|A0PJW6|TM223_HUMAN
        3
        5.4129
        5.3812
        5.4432
        5.4043
        10
        sp|A0PK00|T120B_HUMAN
        1
        4.7299
        4.7075
        4.7537
        4.7216
        11
        sp|A1A4S6|RHG10_HUMAN
        6
        5.5921
        5.5618
        5.6207
        5.5843
        12
        sp|A1IGU5|ARH37_HUMAN
        1
        4.5503
        4.5026
        4.5592
        4.5746
        13
        sp|A1L020|MEX3A_HUMAN
        9
        6.0197
        5.9861
        6.0425
        6.0206
        14
        sp|A1L0T0|HACL2_HUMAN
        14
        6.1952
        6.1741
        6.2165
        6.1891
        15
        sp|A1L157|TSN11_HUMAN
        1
        4.4176
        4.3933
        4.4480
        4.4036
        16
        sp|A1L170|CA226_HUMAN
        1
        4.3816
        4.3458
        4.3944
        4.3942
        17
        sp|A1L390|PKHG3_HUMAN
        5
        5.0416
        4.9997
        5.0701
        5.0423
        18
        sp|A1X283|SPD2B_HUMAN
        36
        6.7276
        6.7006
        6.7492
        6.7250
        19
        sp|A1XBS5|FA92A_HUMAN
        1
        3.8100
        3.7434
        3.8548
        3.8091
        20
        sp|A2AJT9|BCLA3_HUMAN
        3
        5.1031
        5.0738
        5.1206
        5.1064
        21
        sp|A2RRD8|ZN320_HUMAN
        1
        4.3234
        4.2796
        4.3412
        4.3363
        22
        sp|A2RRP1|NBAS_HUMAN
        30
        6.4328
        6.3973
        6.4582
        6.4323
        23
        sp|A2RTX5|SYTC2_HUMAN
        1
        4.1704
        4.1527
        4.1942
        4.1588
        24
        sp|A2RU67|F234B_HUMAN
        2
        4.7223
        4.7009
        4.7357
        4.7243
        25
        sp|A2RUC4|TYW5_HUMAN
        4
        5.0917
        5.0634
        5.1187
        5.0844
        26
        sp|A3KMH1|VWA8_HUMAN
        33
        6.4954
        6.5007
        6.5080
        6.4784
        27
        sp|A3KN83|SBNO1_HUMAN
        18
        6.1917
        6.1675
        6.2129
        6.1877
        28
        sp|A4D161|F221A_HUMAN
        5
        5.2597
        5.2262
        5.2892
        5.2533
        29
        sp|A4D1E9|GTPBA_HUMAN
        5
        5.6777
        5.6564
        5.6990
        5.6714
        30
        sp|A4D1P6|WDR91_HUMAN
        13
        5.9108
        5.8814
        5.9328
        5.9095
        31
        sp|A4D1U4|DEN11_HUMAN
        3
        4.5285
        4.4789
        4.5649
        4.5257
        32
        sp|A4D2B0|MBLC1_HUMAN
        2
        4.1057
        4.0507
        4.1030
        4.1454
        33
        sp|A5D8V6|VP37C_HUMAN
        3
        5.1016
        5.0754
        5.1207
        5.1012
        34
        sp|A5PLL7|PDES1_HUMAN
        2
        5.1872
        5.1639
        5.2028
        5.1883
        35
        sp|A5PLN9|TPC13_HUMAN
        3
        5.6313
        5.5989
        5.6598
        5.6251
        36
        sp|A5YKK6|CNOT1_HUMAN
        53
        6.9213
        6.8919
        6.9446
        6.9186
        37
        sp|A6H8Y1|BDP1_HUMAN
        2
        4.9599
        4.9276
        4.9915
        4.9504
        38
        sp|A6NC57|ANR62_HUMAN
        1
        3.3462
        3.2325
        3.3558
        3.4070
        39
        sp|A6NCE7|MP3B2_HUMAN;sp|Q9GZQ8|MLP3B_HUMAN
        2
        5.3332
        5.3261
        5.3676
        5.3015
        40
        sp|A6NCS6|CB072_HUMAN
        4
        4.7068
        4.6902
        4.7139
        4.7118
        41
        sp|A6ND36|FA83G_HUMAN
        3
        4.9101
        4.9036
        4.9309
        4.8935
        42
        sp|A6NDB9|PALM3_HUMAN
        11
        5.7112
        5.6822
        5.7320
        5.7110
        43
        sp|A6NDE4|RBY1B_HUMAN;sp|A6NEQ0|RBY1E_HUMAN;sp|P0C7P1|RBY1D_HUMAN;sp|P0DJD3|RBY1A_HUMAN;sp|P0DJD4|RBY1C_HUMAN;sp|Q15415|RBY1F_HUMAN
        1
        4.7747
        4.7245
        4.7941
        4.7901
        44
        sp|A6NDG6|PGP_HUMAN
        5
        5.8649
        5.8390
        5.8840
        5.8643
        45
        sp|A6NDU8|CE051_HUMAN
        2
        4.8114
        4.7871
        4.8268
        4.8133
        46
        sp|A6NDX5|ZN840_HUMAN
        1
        4.3298
        4.2876
        4.3691
        4.3187
        47
        sp|A6NED2|RCCD1_HUMAN
        3
        4.7508
        4.7096
        4.7662
        4.7645
        48
        sp|A6NFI3|ZN316_HUMAN
        2
        4.9098
        4.8857
        4.9296
        4.9071
        49
        sp|A6NGN9|IGLO5_HUMAN
        2
        5.0687
        5.0540
        5.0837
        5.0642
        50
        sp|A6NHL2|TBAL3_HUMAN
        1
        4.0439
        3.9831
        4.0709
        4.0582

        Peptide Intensity Distribution

        Peptide intensity per file from mzTab.
        Calculate the average of peptide_abundance_study_variable[1-n] values for each peptide from the peptide table in the mzTab file, and then apply a log2 transformation.
        Created with MultiQC

        MS1 Analysis

        Total Ion Chromatograms

        MS1 quality control information extracted from the spectrum files.
        This plot displays Total Ion Chromatograms (TICs) derived from MS1 scans across all analyzed samples. The x-axis represents retention time, and the y-axis shows the total ion intensity at each time point. Each colored trace corresponds to a different sample. The TIC provides a global view of the ion signal throughout the LC-MS/MS run, reflecting when compounds elute from the chromatography column. Key aspects to assess include: * Overall intensity pattern: A consistent baseline and similar peak profiles across samples indicate good reproducibility. * Major peak alignment: Prominent peaks appearing at similar retention times suggest stable chromatographic performance. * Signal-to-noise ratio: High peaks relative to baseline noise reflect better sensitivity. * Chromatographic resolution: Sharp, well-separated peaks indicate effective separation. * Signal drift: A gradual decline in signal intensity across the run may point to source contamination or chromatography issues. Deviations such as shifted retention times, missing peaks, or inconsistent intensities may signal problems in sample preparation, LC conditions, or mass spectrometer performance that require further investigation.
        Created with MultiQC

        MS1 Base Peak Chromatograms

        MS1 base peak chromatograms extracted from the spectrum files.
        The Base Peak Chromatogram (BPC) displays the intensity of the most abundant ion at each retention time point across your LC-MS run. Unlike the Total Ion Chromatogram (TIC) which shows the summed intensity of all ions, the BPC highlights the strongest signals, providing better visualization of compounds with high abundance while reducing baseline noise. This makes it particularly useful for identifying major components in complex samples, monitoring dominant species, and providing clearer peak visualization when signal-to-noise ratio is a concern. Comparing BPC patterns across samples allows you to evaluate consistency in the detection of high-abundance compounds and can reveal significant variations in sample composition or instrument performance.
        Created with MultiQC

        MS1 Peaks

        MS1 Peaks from the spectrum files
        This plot shows the number of peaks detected in MS1 scans over the course of each sample run. The x-axis represents retention time (in minutes), while the y-axis displays the number of distinct ion signals (peaks) identified in each MS1 scan. The MS1 peak count reflects spectral complexity and provides insight into instrument performance during the LC-MS analysis. Key aspects to consider include: * Overall pattern: Peak counts typically increase during the elution of complex mixtures and decrease during column washing or re-equilibration phases. * Peak density: Higher counts suggest more complex spectra, potentially indicating a greater number of compounds present at that time point." * Peak Consistency across samples: Similar profiles among replicates or related samples indicate good analytical reproducibility. * Sudden drops: Abrupt decreases in peak count may point to transient ionization issues, spray instability, or chromatographic disruptions. * Baseline values: The minimum peak count observed reflects the level of background noise or instrument sensitivity in the absence of eluting compounds. Monitoring MS1 peak counts complements total ion chromatogram (TIC) and base peak chromatogram (BPC) data, offering an additional layer of quality control related to signal complexity, instrument stability, and sample composition.
        Created with MultiQC

        General stats for MS1 information

        General stats for MS1 information extracted from the spectrum files.
        This table presents general statistics for MS1 information extracted from mass spectrometry data files." It displays MS runs with their acquisition dates and times. For each file, the table shows two key metrics: TotalCurrent (the sum of all MS1 ion intensities throughout the run) and ScanCurrent (the sum of MS2 ion intensities). These values provide a quick overview of the total ion signals detected during both survey scans (MS1) and fragmentation scans (MS2), allowing for comparison of overall signal intensity across samples. Consistent TotalCurrent and ScanCurrent values across similar samples typically indicate good reproducibility in the mass spectrometry analysis, while significant variations may suggest issues with sample preparation, instrument performance, or ionization efficiency. The blue shading helps visualize the relative intensity differences between samples.
        Showing 11/11 rows and 3/3 columns.
        FileAcquisition Date Timelog10(Total Current)log10(Scan Current)
        a05058
        2017-04-05 00:38:45
        13.3186
        10.7826
        a05059
        2017-04-05 04:14:11
        13.1799
        10.6932
        a05060
        2017-04-05 07:49:24
        13.3878
        10.7992
        a05061
        2017-04-05 11:24:37
        13.4434
        10.8149
        a05062
        2017-04-05 15:00:06
        13.3927
        10.8790
        a05063
        2017-04-05 18:32:46
        13.3876
        10.9186
        a05064
        2017-04-05 22:05:41
        13.4376
        10.9428
        a05065
        2017-04-06 10:43:06
        13.3235
        10.8011
        a05066
        2017-04-06 14:15:46
        13.4091
        10.8419
        a05067
        2017-04-06 17:48:21
        13.4054
        10.8081
        a05068
        2017-04-06 21:21:20
        12.7353
        10.4647

        MS2 and Spectral Stats

        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment.
        This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).
        Created with MultiQC

        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.
        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.
        Created with MultiQC

        Pipeline Spectrum Tracking

        This plot shows the tracking of the number of spectra along the quantms pipeline
        This table shows the changes in the number of spectra corresponding to each input file during the pipeline operation. And the number of peptides finally identified and quantified is obtained from the PSM table in the mzTab file. You can also remove decoys with the `remove_decoy` parameter.: * MS1_Num: The number of MS1 spectra extracted from mzMLs * MS2_Num: The number of MS2 spectra extracted from mzMLs * MSGF: The Number of spectra identified by MSGF search engine * Comet: The Number of spectra identified by Comet search engine * Sage: The Number of spectra identified by Sage search engine * PSMs from quant. peptides: extracted from PSM table in mzTab file * Peptides quantified: extracted from PSM table in mzTab file
        Showing 11/11 rows and 6/6 columns.
        Spectra File#MS1 Spectra#MS2 SpectraMSGFComet#PSMs from quant. peptides#Peptides quantified
        a05058
        6870
        37330
        24401
        24117
        13935
        11763
        a05059
        6471
        37536
        24920
        24702
        14683
        13183
        a05060
        7266
        38047
        24711
        24518
        14027
        12475
        a05061
        7649
        37733
        24986
        24790
        14661
        12995
        a05062
        5657
        41445
        26957
        26729
        15121
        13672
        a05063
        5982
        42856
        29469
        29222
        18909
        17336
        a05064
        7075
        40809
        27720
        27427
        17667
        15735
        a05065
        5947
        40698
        26495
        26127
        15303
        13323
        a05066
        6051
        41078
        26992
        26761
        15630
        13808
        a05067
        6062
        39997
        26228
        25738
        14819
        13170
        a05068
        4297
        36307
        17727
        17634
        17
        12

        Distribution of Precursor Charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.
        This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.
        Created with MultiQC

        Charge-state of Per File

        The distribution of precursor ion charge states (based on mzTab data).
        The distribution of precursor ion charge states (based on mzTab data).
        Created with MultiQC

        MS/MS Counts Per 3D-peak

        An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file.
        For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.
        Created with MultiQC


        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        CONSENSUSIDConsensusID3.3.0-pre-exported-20250122
        DECOYDATABASEDecoyDatabase3.3.0-pre-exported-20250122
        FDRCONSENSUSIDFalseDiscoveryRate3.3.0-pre-exported-20250122
        IDFILTERIDFilter3.3.0-pre-exported-20250122
        MS2RESCOREMS2Rescore3.1.4
        deeplc3.1.8
        ms2pip4.1.0
        quantms-rescoring0.0.5
        MSGFDBINDEXINGmsgf_plusMS-GF+ Release (v2024.03.26) (26 March 2024)
        MZMLSTATISTICSquantms-utils0.0.20
        PERCOLATORPercolatorAdapter3.3.0-pre-exported-20250122
        percolator3.05.0, Build Date Aug 31 2020 19:03:04
        PROTEOMICSLFQProteomicsLFQ3.3.0-pre-exported-20250122
        SAMPLESHEET_CHECKquantms-utils0.0.20
        SDRFPARSINGsdrf-pipelines0.0.31
        SEARCHENGINECOMETComet2023.01 rev. 2
        CometAdapter3.3.0-pre-exported-20250122
        SEARCHENGINEMSGFMSGFPlusAdapter3.3.0-pre-exported-20250122
        msgf_plusMS-GF+ Release (v2023.01.12) (12 January 2023)
        THERMORAWFILEPARSERThermoRawFileParser1.3.4
        WorkflowNextflow24.10.5
        bigbio/quantmsv1.4.0dev

        bigbio/quantms Workflow Summary

        Input/output options

        export_decoy_psm
        true
        input
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/PXD007683-LFQ.sdrf.tsv
        local_input_type
        raw
        outdir
        ./PXD007683_msgf_comet_ms2rescore_quant
        root_folder
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683

        SDRF validation

        skip_factor_validation
        true
        use_ols_cache_only
        true
        validate_ontologies
        true

        Protein database

        add_decoys
        true
        database
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/uniprot-UP000005640_UP000002311_reviewed.fasta

        Database search

        search_engines
        msgf,comet

        Modification localization

        luciphor_debug
        0

        PSM re-scoring (general)

        ms2pip_model
        HCD
        ms2pip_model_dir
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/model_file/
        ms2rescore
        true
        run_fdr_cutoff
        0.10

        PSM re-scoring (Percolator)

        description_correct_features
        0

        Consensus ID

        consensusid_considered_top_hits
        0
        min_consensus_support
        0

        Isobaric analyzer

        quant_activation_method
        HCD

        Protein Quantification (LFQ)

        feature_with_id_min_score
        0.10

        Statistical post-processing

        contrasts
        pairwise
        skip_post_msstats
        true

        Quality control

        enable_pmultiqc
        true
        pmultiqc_idxml_skip
        true

        Generic options

        trace_report_suffix
        2025-03-30_11-14-03

        Core Nextflow options

        configFiles
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms/nextflow.config, /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/k8s_next.config
        containerEngine
        docker
        launchDir
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms
        profile
        docker
        projectDir
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms
        runName
        mad_hodgkin
        userName
        daicx
        workDir
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms/work

        bigbio/quantms Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/bigbio/quantms

        Methods

        Data was processed using bigbio/quantms v1.4.0dev (doi: 10.5281/zenodo.7754148) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.10.5 (Di Tommaso et al., 2017) with the following command:

        nextflow run main.nf --input /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/PXD007683-LFQ.sdrf.tsv --database /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/uniprot-UP000005640_UP000002311_reviewed.fasta --add_decoys true --search_engines msgf,comet -resume PXD007683 -c /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/k8s_next.config -profile docker --ms2rescore true --ms2pip_model_dir /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/model_file/ --root_folder /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683 --local_input_type raw --ms2pip_model HCD --outdir ./PXD007683_msgf_comet_ms2rescore_quant --skip_post_msstats true

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.